Once data has been gathered, it must be cleaned, processed, and analyzed in order to find the most appropriate model to answer bioinformatics questions. Using case studies from biological data, health records and textual analysis, students learn concepts and techniques of data mining and machine learning. They use a variety of classification, regression, and clustering algorithms to extract frequent patterns and outliers within the data.

Priority registration in this course is offered to students admitted to the Bachelor of Science in Bioinformatics.

Prerequisite(s): A minimum "C" grade in all of the following: CPSC 2150, 3260, MATH 1252, and STAT 3225.